Analysis of the Status of Research Outputs in the Field of Knowledge Management in the Health and Healthcare Field
Bibliographic record
Abstract
Background and Aim: The expansion of information technology has led to the production of increasing knowledge, which may be a part of this knowledge that is hidden, so the role of knowledge management is very important to reveal knowledge. On the other hand, in health research, which is basically based on the needs of patients, their caregivers, and specialists, knowledge management is of great importance for the quality of their services. The aim of the current research is to analyze the status of research outputs in the field of knowledge management in the health sector. Materials and Methods: Based on its nature, the present study is descriptive, quantitative, and applied, and was conducted using a lexical co-occurrence scientometric technique. The research community includes 2487 sources, which are the results of all research outputs in the field of knowledge management in the health sector, which are indexed in the Web of Science database. The analysis of the research questions was done through Excel, BibExcel, and VOSviewer software. Results: According to research findings, the continents of Europe, Asia, and North America, respectively, have had the highest contributions to research output in the field of knowledge management in the health and healthcare sector. Among individual countries, the United States, the United Kingdom, and Canada demonstrated the most significant activity in this area, while Iran ranked 17th. Among the United Nations Sustainable Development Goals (SDGs), the goals of Good Health and Well-being, Industry, Innovation and Infrastructure, and Quality Education have received the most attention in knowledge management research related to health and healthcare. The keyword co-occurrence map highlights the prominence of terms such as “knowledge management,” “healthcare,” and “electronic health records.” The identified thematic clusters also underscore the significance of three key domains: organizational performance, information management, and health information systems. Conclusion: In developed countries and the first level of the world, attention to knowledge management in the field of health and health is more prominent. Also, in order to achieve a high level in the field of health and health as an important and effective criterion in most development sectors, it is necessary to address other sustainable development goals, especially by establishing systems Knowledge management in the field of health helped to achieve important goals such as eradicating poverty and hunger and reducing inequalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.195 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.049 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".